10 papers · 1 filter
LLaVA-PruMerge: Adaptive Token Reduction for Efficient Large Multimodal Models
Yuzhang Shang, Mu Cai, Bingxin Xu +2
Large Multimodal Models (LMMs) have shown significant visual reasoning capabilities by connecting a visual encoder and a large language model. LMMs typically take in a fixed and la…
Distill Video Datasets into Images
Zhenghao Zhao, Haoxuan Wang, Kai Wang +3
Dataset distillation aims to synthesize compact yet informative datasets that allow models trained on them to achieve performance comparable to training on the full dataset. While…
Efficient Multimodal Dataset Distillation via Generative Models
Zhenghao Zhao, Haoxuan Wang, Junyi Wu +3
Dataset distillation aims to synthesize a small dataset from a large dataset, enabling the model trained on it to perform well on the original dataset. With the blooming of large l…
QuEST: Low-bit Diffusion Model Quantization via Efficient Selective Finetuning
Haoxuan Wang, Yuzhang Shang, Zhihang Yuan +3
The practical deployment of diffusion models is still hindered by the high memory and computational overhead. Although quantization paves a way for model compression and accelerati…
CaO: Rectifying Inconsistencies in Diffusion-Based Dataset Distillation
Haoxuan Wang, Zhenghao Zhao, Junyi Wu +3
The recent introduction of diffusion models in dataset distillation has shown promising potential in creating compact surrogate datasets for large, high-resolution target datasets,…
DKDM: Data-Free Knowledge Distillation for Diffusion Models with Any Architecture
Qianlong Xiang, Miao Zhang, Yuzhang Shang +3
Diffusion models (DMs) have demonstrated exceptional generative capabilities across various domains, including image, video, and so on. A key factor contributing to their effective…